| Study on land productivity potential of maize in Jilin Province based on spatial interpolation technique and auxiliary information |
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| DOI：10.7606/j.issn.1000-7601.2011.05.38 |
| Key Words: potential land productivity spatial interpolation auxiliary information GIS |
| Author Name | Affiliation | | SHI Shuqin | School of Management, Tianjin Polytechnic University, Tianjin 300387, China | | CHEN Youqi | Key Laboratory of Resources Remote Sensing & Digital Agriculture of Ministry of Agriculture, Beijing 100081, China
Institute of Agricultural Resources & Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081 | | LI Zhengguo | Key Laboratory of Resources Remote Sensing & Digital Agriculture of Ministry of Agriculture, Beijing 100081, China
Institute of Agricultural Resources & Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081 | | YANG Peng | Key Laboratory of Resources Remote Sensing & Digital Agriculture of Ministry of Agriculture, Beijing 100081, China
Institute of Agricultural Resources & Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081 | | WU Wenbin | Key Laboratory of Resources Remote Sensing & Digital Agriculture of Ministry of Agriculture, Beijing 100081, China
Institute of Agricultural Resources & Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081 | | TANG Fang | Wuhan Land Reserve and Management Center, Wuhan, Hubei 430010, China |
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| Abstract: |
| In this study, by using spatial analysis function of ArcGIS software, a crop productivity potential model originally from an attenuation model has been developed to assess maize land productivity potential in Jilin Province located in northeast China. Moreover, spatial interpolation technique and auxiliary information are used for spatial modeling of key climate and soil conditions, which are required in simulating light and temperature potential productivity. First, temperature and precipitation data observed from climate data stations in Jilin are interpolated by using an integrated means of multiple regression and residual error interpolation. Second, with a consideration of soil type information, the relevant factors are utilized as co factors for interpolating soil properties (i.e. soil pH, soil organic matter, available K, alkali hydrolyzable N and available P) by using the means of Cokriging technique. Finally, based on temperature, moisture and soil correction coefficients, the maize land productivity potential in Jilin are reclassified and zoned into different yield levels. |
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